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Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

Australian Ocean Data Network
Li, J. ; Potter, A. ; Huang, Z. ; Heap, A.
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://pid.geoscience.gov.au/dataset/ga/74030&rft.title=Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods&rft.identifier=https://pid.geoscience.gov.au/dataset/ga/74030&rft.publisher=Geoscience Australia&rft.description=In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content for the AEEZ using samples extracted on August 2010 from Geoscience Australia's Marine Samples Database. The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region but the choice of mtry. No single method performs best for all the tested scenarios. Of the 18 compared methods, RFIDS and RFOK are the most accurate methods in all three regions. Overall, of the 36 combinations of input secondary variables, methods and regions, RFIDS, 6RFIDS and RFOK were among the most accurate methods in all three regions. Model averaging further improved the prediction accuracy. The most accurate methods reduced the prediction error by up to 7%. RFOKRFIDS, with a search window size of 5, an mtry of 4 and more realistic predictions in comparison with the control, is recommended for predicting sand content across the AEEZ if a single method is required. This study provides suggestions and guidelines for improving the spatial interpolations of marine environmental data.Maintenance and Update Frequency: unknownStatement: Unknown&rft.creator=Li, J. &rft.creator=Potter, A. &rft.creator=Huang, Z. &rft.creator=Heap, A. &rft.date=2012&rft_rights=&rft_rights=Creative Commons Attribution 4.0 International Licence&rft_rights=CC-BY&rft_rights=4.0&rft_rights=http://creativecommons.org/licenses/&rft_rights=WWW:LINK-1.0-http--link&rft_rights=Australian Government Security ClassificationSystem&rft_rights=https://www.protectivesecurity.gov.au/Pages/default.aspx&rft_rights=WWW:LINK-1.0-http--link&rft_rights=Creative Commons Attribution 4.0 International Licence http://creativecommons.org/licenses/by/4.0&rft_subject=geoscientificInformation&rft_subject=GA Publication&rft_subject=Record&rft_subject=environmental&rft_subject=geoscience&rft_subject=seabed&rft_subject=numerical modelling&rft_subject=marine&rft_subject=EARTH SCIENCES&rft_subject=Published_External&rft.type=dataset&rft.language=English Access the data
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Creative Commons Attribution 4.0 International Licence
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Creative Commons Attribution 4.0 International Licence

CC-BY

4.0

http://creativecommons.org/licenses/

WWW:LINK-1.0-http--link

Australian Government Security ClassificationSystem

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WWW:LINK-1.0-http--link

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Brief description

In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content for the AEEZ using samples extracted on August 2010 from Geoscience Australia's Marine Samples Database. The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region but the choice of mtry. No single method performs best for all the tested scenarios. Of the 18 compared methods, RFIDS and RFOK are the most accurate methods in all three regions. Overall, of the 36 combinations of input secondary variables, methods and regions, RFIDS, 6RFIDS and RFOK were among the most accurate methods in all three regions. Model averaging further improved the prediction accuracy. The most accurate methods reduced the prediction error by up to 7%. RFOKRFIDS, with a search window size of 5, an mtry of 4 and more realistic predictions in comparison with the control, is recommended for predicting sand content across the AEEZ if a single method is required. This study provides suggestions and guidelines for improving the spatial interpolations of marine environmental data.

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Maintenance and Update Frequency: unknown
Statement: Unknown

Issued: 2012

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Earth Sciences | GA Publication | Published_External | Record | environmental | geoscience | geoscientificInformation | marine | numerical modelling | seabed |

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Download the Record (pdf)

uri : https://d28rz98at9flks.cloudfront.net/74030/Rec2012_048.pdf

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